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Sushma Kukkadapu’s account of building a financial-services monitoring dashboard with OpenObserve shows how a team can bring Java and Node.js service signals into one view. Her implementation used OpenTelemetry instrumentation, OpenObserve Query Language (OQL), visualizations and an alert for rising API failures. The results she reports are her team’s experience, not independently verified benchmarks or guaranteed outcomes.
Why Kukkadapu built a unified dashboard
Kukkadapu describes a publish API outage that was difficult to diagnose because relevant monitoring information was spread across separate tools. Recalling the moment, she says she told a colleague, “There has to be a better way.” Her response was to build a shared monitoring view for financial services rather than continue investigating service health across disconnected dashboards.
She says OpenObserve was selected for a unified approach to logs, metrics and traces, along with cost efficiency and flexibility for a mixed Java and Node.js stack. Her DZone account does not name other platforms the team considered or provide a comparative cost study, so it does not establish that OpenObserve is cheaper or better than a particular alternative. Read Kukkadapu’s account on DZone.
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The implementation focused on service and API signals that could help the team spot failures and resource pressure. Kukkadapu describes instrumenting both Java and Node.js applications with OpenTelemetry counters, then using OpenObserve to query and visualize the resulting data.
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| Signal | How it was used in the account |
|---|---|
| API successes and failures | OpenTelemetry counters tracked outcomes; OQL queries supported API success-rate charts and an alert for increased failure rates. |
| API calls and errors | Counters captured request activity and errors to help expose service behavior. |
| Request duration | Included among the monitored examples described in the article. |
| CPU utilization | OQL queries supported CPU-utilization charts, helping the team identify resource-intensive work. |
These are examples from Kukkadapu’s article; the described queries and implementation have not been independently tested here. The account does not supply a complete instrumentation configuration, schema, or reproducible deployment guide.
How the implementation fits together
Instrument the Java and Node.js services
The first layer was application instrumentation. Kukkadapu says the team used OpenTelemetry counters to record successes, failures, API calls and errors across its Java and Node.js services. This made application outcomes available as metrics for the dashboard rather than leaving the team to infer service behavior from unrelated screens.
Rank #2
Query the signals with OQL
With the data available, the team used OpenObserve Query Language to build queries for API success rates and CPU utilization. These queried views were intended to turn collected telemetry into operational signals the team could inspect; the source does not publish enough query detail to reproduce exact OQL statements.
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Build visualizations and add an alert
The queried data fed dashboard visualizations, while an alert was configured for an increase in API failure rates. That combination matters: charts help people examine trends, and an alert can draw attention to a condition that warrants investigation. The article does not specify the alert threshold, evaluation interval, notification channel, or escalation policy.
Rank #3
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Results Kukkadapu reports
Kukkadapu attributes the following outcomes to the monitoring work in her 2025 DZone account. They are reported results from her team, not independently verified measurements; the article’s summary does not provide a measurement protocol or enough detail to generalize them to another deployment.
| Reported outcome | Attribution and qualification |
|---|---|
| 40% reduction in CPU use | Kukkadapu says monitoring helped identify an endpoint that the team optimized; she attributes the reduction to that work. |
| Typical diagnosis time from more than 3 hours to less than 1 hour | Kukkadapu reports this change in the team’s diagnosis time. |
| 22% reduction in cloud infrastructure costs | Kukkadapu attributes the reduction to identifying and optimizing resource-intensive operations. |
These figures describe one team’s experience. They should not be read as expected savings, a product benchmark, or a promise that adopting the same dashboard will produce the same outcome.
Rank #4
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What was planned, not completed
Kukkadapu describes distributed tracing, machine-learning anomaly detection and correlations with business outcomes as future plans. They are not established as completed features of this project in her account. Readers should distinguish these proposed next steps from the Java and Node.js instrumentation, OQL queries, charts and failure-rate alert she describes as part of the dashboard.
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The article is useful as a first-person example of organizing financial-service monitoring around application telemetry, queries, visualizations and an alert. It does not independently confirm OpenObserve’s current capabilities or pricing, quantify the cost comparison behind the selection, or provide enough technical detail to reproduce the setup exactly. Teams evaluating a similar design would need to verify current product details and determine their own instrumentation, query, alerting and operational requirements.
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